Method and control system for controlling an air-conditioning system

This application claims the benefit of priority of U.S. Provisional Patent Application No. 63/005,483, filed on 6 Apr. 2020, the content of which being hereby incorporated by reference in its entirety for all purposes.

The present invention generally relates to a method of controlling an air-conditioning system associated with a building and a control system thereof, and more particularly, for optimizing a plurality of building performance parameters in providing an environment (e.g., a desired indoor environment) with respect to a zone of the building.

Buildings consume significant energy and the air-conditioning system such as Heating, Ventilation and Air Conditioning (HVAC) systems contribute to significant proportion of such consumption. Commercial HVAC systems are either Variable Air Volume (VAV) or Variable Refrigerant Volume (VRV)-type systems supplying cooling energy to multiple zones. The controllers for such systems can vary from being a simple thermostat to an optimization-based controller (e.g., Model Predictive Control). Many HVAC control methods have a centralized architecture and aim to minimize energy consumption across all zones using MPC due to its ability to handle complicated constraints, nonlinear dynamics, and physical behaviors. However, due to computational difficulties with a large number of zones and implementation issues centralized control architecture is unsuitable due to computation complexity. Therefore, decentralized control methods have been proposed to overcome this problem. Several methods previously disclosed for improving control of an air-conditioning system for buildings using various control methods will now be briefly mentioned below.

U.S. Pat. No. 9,568,924 B2, by Clifford C. Federspiel (2017), “Methods and Systems for Coordinating the Control of HVAC Units”, describes a control technique wherein a supervisory controller receives feedback signal from a plurality of environmental sensors and uses a set of reference values to determine the control signals that actuate HVAC units using a pseudo-inverse of a transfer function matrix. Then the control signals are computed using the transfer function matrix from error signals (the difference between feedback and reference). The method described had a limited fault-diagnosis capability as well. U.S. Pat. No. 8,521,332 B2, by Tiemann et al. (2013), “Actuator for HVAC systems and method for operating the actuator”, proposed a method for operating the actuator comprising of a network interface for connecting the actuator to sensor/actuator bus, a data store and a processor connected to a data store. However, the method does not deal with the control technique being used.

A method to update real-time values function estimates through parallel and reinforcement learning was proposed in U.S. Patent Publication No. 2016/0223218 A1, by Barret, Enda (2013), “Automated control and parallel learning HVAC apparatuses, methods and systems”. The objective was to maximize quality of experience and minimize energy in regulated environmental spaces by coordinating thermostat set-points.

The use of monitoring units that gather information from multiple locations to define the heating/cooling demand in different locations was proposed in U.S. Pat. No. 6,865,449 B2, by Dudley, Kevin F. (2005), “Location adjusted HVAC control”. U.S. Pat. No. 6,868,900 B2, by Dage et al. (2005), “Multiple zone automatic HVAC control system and method” proposed a multiple zone automatic HVAC control system and method for vehicles. The control system had plurality of sensors and mechanisms to control the temperature and flow of air from the HVAC system into multiple zones. PCT International Publication No. WO 01/57489 A1, by Kline et al. WO (2001), “HVAC control using internet” described an approach to control the HVAC system using the Internet. This patent application publication proposed an apparatus and process for controlling the HVAC system.

A low cost and easy to install zone climate control system for retrofitting an existing forced air HVAC system, that can provide independent minute-by-minute, day-by-day and room-by-room climate control was proposed in U.S. Pat. No. 6,997,390 B2, by Alles (2006), “Retrofit HVAC zone climate control system”. This U.S. patent publication provided options for the users to program the set-points, specify temperature schedules, providing local temperature control, and display energy usage for different comfort requirements. An environmental control system for a plurality of zones within a building with a plurality HVAC unit was proposed in U.S. Pat. No. 7,809,472 B2, by Silva et al. (2010), “Control system for multiple heating, ventilation and air conditioning units”. In this patent publication, multiple controllers were connected to the thermostat for controlling the HVAC system unit in accordance with an output from the temperature sensor. A link interconnects the plurality of the sensors into a network. The device can be networked and can be operated in overlap or non-overlap mode.

A method to use multiple schedules that are entered using a user interface for HVAC system and a controller unit for commanding them was presented in U.S. Pat. No. 8,185,245 B2, by Amundson et al. (2012), “HVAC control with utility time of day pricing support”. A wireless remote terminal that includes a transmitter for sending information to an HVAC electronic controller and to one additional remote terminal, a receiver adapted for receiving the information was proposed in U.S. Patent Publication No. 2006/0097063 A1, by Zeevi (2006), “Modular HVAC control system”. A method to control HVAC systems based on occupancy information was described in U.S. Patent Publication No. 2008/0277486 A1, by Seem et al. (2008), “HVAC control system and method”. An occupancy-based demand controlled ventilation was described in U.S. Patent Publication No. 2011/0127340 A1, by Aiken, Thomas D. (2011), “Occupancy-based demand controlled ventilation system”. Further, U.S. Patent Publication No. 2013/0085614 A1, by Wenzel et al. (2013), “Systems and methods for controlling energy use in a building management system using energy budgets” designed a feedback controller that generates manipulated variables based on energy use set-points and measured energy use.

A cloud enabled building automation system wherein information can be received from the cloud through user interfaces and the generation of optimized control signals was described in U.S. Patent Publication No. 2013/0274940 A1, by Wei et al. (2013), “Cloud enabled building automation system”. Creating a localized dynamic system for HVAC control in zones was described in U.S. Patent Publication No. 2014/0379141 A1, by Patil et al. (2014), “Zone based heating, ventilation and air-conditioning (HVAC) control using extensive temperature monitoring”.

A need therefore exists to provide a method of controlling an air-conditioning system associated with a building and a control system thereof, that seek to overcome, or at least ameliorate, one or more of the deficiencies in conventional methods or control systems for controlling air-conditioning system(s), such as but not limited to, enhancing building performances in providing an environment (e.g., a desired indoor environment) in a zone of a building, and more particularly, in a flexible and decentralized manner with significant energy saving. It is against this background that the present invention has been developed.

According to a first aspect of the present invention, there is provided a method of controlling an air-conditioning system associated with a building for optimizing a plurality of building performance parameters in providing an environment with respect to a zone of the building, using at least one processor, the method comprising:

According to a second aspect of the present invention, there is provided a control system for controlling an air-conditioning system associated with a building for optimizing a plurality of building performance parameters in providing an environment with respect to a zone of the building, the control system comprising:

Various embodiments of the present invention provide a method of controlling an air-conditioning system associated with a building and a control system thereof, and more particularly, for optimizing a plurality of building performance parameters in providing an environment (e.g., a desired indoor environment) with respect to a zone of the building. It will be appreciated by a person skilled in the art that the above-mentioned zone may refer to any one or more regions or enclosures or enclosed areas within a building, such as but not limited to, a room (e.g., an office room, a meeting room, an apartment room, a hotel room and so on), an open-plan office space, a lecture hall, a theatre, so on. It will be appreciated by a person skilled in the art that the above-mentioned environment may refer an indoor environment within the zone conditioned or regulated by the air-conditioning system. It will also be appreciated by a person skilled in the art that the method of controlling an air-conditioning system and a control system thereof, for optimizing a plurality of building performance parameters in providing an environment with respect to a zone of the building, may also be applied or employed with respect to each zone (e.g., each predetermined or selected zone) of the building. Accordingly, the building performance parameters with respect to each zone of the building may be optimized.

depicts a flow diagram of a methodof controlling an air-conditioning system associated with a building for optimizing a plurality of building performance parameters in providing an environment with respect to a zone of the building, using at least one processor. The methodcomprises: obtaining (at), zone environmental condition information including zone temperature data associated to the zone, and cooling air temperature data associated to an air handling unit associated to the zone; obtaining (at), from a zone model generator, zone cooling load parameters associated to the zone with respect to a plurality of time periods (which may also be interchangeably referred to herein as time intervals) and a zone thermal dynamic model; obtaining (at), from a scheduler, a sequence of optimal cool air supply rates with respect to a plurality of subsequent time periods with respect to the zone determined based on a multi-component cost function including a plurality of components relating to the plurality of building performance parameters; determining, (at), based on the zone thermal dynamic model, a sequence of zone controller set-points (with respect to the plurality of subsequent time periods with respect to the zone) corresponding to the sequence of optimal cool air supply rates with respect to the zone using the zone cooling load parameters, the sequence of optimal cool air supply rates, the zone temperature data and the cooling air temperature data associated to the air handling unit; and sending (at), the sequence of zone controller set-points to a zone controller for controlling a temperature of the zone.

In various embodiments, the time periods or intervals may refer to instants of time or time instants.

In various embodiments, the above-mentioned providing an environment with respect to a zone of the building may refer to conditioning or regulating the environment in or within the zone.

In various embodiments, the above-mentioned air-conditioning system may include, but is not limited to, a heating, ventilation and air-conditioning (HVAC) system. It will be appreciated that the present invention is not limited to any particular or specific air-conditioning system, as long as it is capable of being controlled based on inputs to condition or regulate the environment in the zone at least with respect to temperature.

In various embodiments, the above-mentioned obtaining zone environmental condition information including zone temperature data associated to the zone comprises obtaining, from a zone sensor module, zone temperature measurement data associated to the zone with respect to a current time (e.g., zone ambient temperature measurement associated to the zone).

In various embodiments, the above-mentioned subsequent time periods may be future time periods.

In various embodiments, the above-mentioned obtaining zone environmental condition information including zone temperature data associated to the zone comprises obtaining, from the zone model generator, zone temperature data associated to the zone with respect to the plurality of subsequent time periods. Accordingly, the zone temperature data associated to the zone with respect to the plurality of subsequent time periods may be predicted zone temperature associated to the zone with respect to future time periods.

In various embodiments, the above-mentioned obtaining cooling air temperature data associated to an air handling unit associated to the zone comprises obtaining the cooling air temperature measurement data associated to the air handling unit associated to the zone with respect to the current time. The cooling air temperature associated to the air handling unit associated to the zone may be obtained from a Building Energy Management System or one or more sensors located at the air handling unit. For example, a Building Energy Management System may include a sensor installed in the air handling unit for measuring the cooling air temperature.

In various embodiments, the zone thermal dynamic model is trained by the model generator based on measured data of zone temperature associated to the zone, zone cool air supply rate associated to the zone and cooling air temperature associated to the air handling unit associated to the zone. In various embodiments, the zone thermal dynamic model is trained by the model generator based on the measured data for predicting the zone temperature associated to the zone with respect to subsequent discrete time instants.

In various embodiments, the above-mentioned zone controller set-points may be thermal set-points. In various embodiments, the sequence of zone controller set-points may be a schedule of zone controller set-points (e.g., zone controller set-points over a prediction horizon such as 21° C. at 10 am, 22° C. at 10:15 am, 24° C. at 10:30 am, etc).

In various embodiments, the plurality of components of the multi-component cost function comprise a first component relating to zone occupancy associated to the zone determined based on a zone occupancy detection model, a second component relating to fan power of the air handling unit determined based on a fan power function, a third component relating to chiller power determined based on a chiller power function, a fourth component relating to coupling of a pressure of a supply fan associated to the air handling unit and zone air flow rates corresponding to zones associated to the air handling unit determined based on a coupling function in relation to the pressure of the supply fan associated to the air handling unit and the zone air flow rates corresponding to zones associated to the air handling unit.

In various embodiments, the zone thermal dynamic model, the zone occupancy detection model, the fan power function, the chiller power function and the coupling function in relation to the pressure of the supply fan associated to the air handling unit and/or the zone air flow rates corresponding to the zones associated to the air handling unit may involve training (e.g., is produced by being trained) in the zone model generator based on labelled data to make a prediction or estimation (output) for a given input. In a non-limiting example, the zone thermal dynamic model may be learned in the zone model generator based on a linear regression model with the least squares estimation method. For example, the zone occupancy detection model, the fan power function, the chiller power function and the coupling function in relation to the pressure of the supply fan associated to the air handling unit and/or the zone air flow rates corresponding to the zones associated to the air handling unit may be derived via machine learning methods. In other embodiments, non-linear regression models may be used to describe the fan power function and the chiller power function. The zone thermal dynamic model, the zone occupancy detection model, the fan power function, the chiller power function and the coupling function in relation to the pressure of the supply fan associated to the air handling unit and/or the zone air flow rates corresponding to the zones associated to the air handling unit may be learned models in the zone model generator based on measured data.

In various embodiments, the zone cooling load parameters associated to the zone with respect to the plurality of time periods may be determined based on the learned zone thermal dynamic model in the zone model generator. For example, the zone cooling load parameters associated to the zone may be with respect to the current time period and the subsequent time periods (e.g., Q(t), . . . , Q(t+K)). Accordingly, the above-mentioned obtaining, from a zone model generator, zone cooling load parameters associated to the zone with respect to a plurality of time periods may include obtaining zone cooling load parameters associated to the zone with respect to the current time period and the subsequent time periods.

In various embodiments, a zone cooling load parameter of the above-mentioned zone cooling load parameters may refer to the amount of heat energy that need to be removed from a given space to maintain the temperature in an acceptable range. For example, the zone cooling load parameter (or ambient cooling load) may refer to an amount of heat energy accumulated within some time interval. The value of a zone cooling load parameter Q(t) may refer to the specific cooling load value measured at time instant t.

In various embodiments, the ambient cooling load may be assumed to be captured by a piecewise constant function, that is, its value maintains a constant over a certain time period and may changes to another constant for the next time period. This piecewise constant function may be determined based on a standard parameter estimation algorithm within the zone model generator.

In various embodiments, the plurality of components of the multi-component cost function further comprise a component (fifth component) relating to respective zone cool air supply rate requests corresponding to the zone and one or more other zones in the building with respect to the plurality of subsequent time periods.

In various embodiments, the plurality of components of the multi-component cost function further comprise a component (sixth component) relating to occupant thermal comfort.

In various embodiments, the component relating to occupant thermal comfort comprises a thermal set-point obtained from a predetermined value, predicted based on an occupant thermal comfort prediction model or obtained from user input.

In various embodiments, the methodfurther comprises predicting, based on the occupant thermal comfort prediction model, the occupant thermal comfort using the zone temperature data, zone humidity data, zone carbon dioxide concentration data and zone cool air supply rate data associated to the zone obtained from the zone sensor module.

In various embodiments, the zone controller comprises a zone variable air volume (VAV) controller. In various embodiments, the zone controller set-points may be actual control signals which is sent to the zone HVAC variable air volume controller associated to the zone. The zone controller set-points may be zone thermal set-points which is a range of temperature. For example, the zone variable air volume controller may adjust the variable air volume damper to ensure that the zone temperature of the zone will reach the zone thermal set-points, which indirectly reflect the sequence of optimal cool air supply rates with respect to a plurality of subsequent time periods (or cooling air supply schedule) from the scheduler. The damper may be located inside the zone VAV box (e.g., not the damper in the AHU).

In various embodiments, the plurality of building performance parameters may include a building energy efficiency parameter and an occupant thermal comfort parameter.

In various embodiments, the above-mentioned scheduler may solve a scheduling problem (e.g., corresponding to the “multi-component cost function” described hereinbefore according to various embodiments) for obtaining a sequence of optimal cool air supply rates with respect to a plurality of subsequent time periods for each of a plurality of zones in the building based on the multi-component cost function to optimize the plurality of building performance parameters (e.g., of building energy efficiency and occupant thermal comfort) in providing the environment (e.g., desired indoor environment) with respect to the zones in the building. The scheduler may be based on a distributed model predictive control (MPC) scheme such as described in PCT Application No. PCT/SG2016/050122 published as PCT International Publication No. WO 2016/148651 A1, by Rong et al. (2016), “Method of operating a building environment management system”, which provides a scalable distributed scheduling and control approach for HVAC systems.

Accordingly, various embodiments provide an implementation framework of data collection and analysis that facilitates deployment of the distributed model predictive controller (MPC) for HVAC control. The methodof controlling an air-conditioning system associated with a building according to various embodiments of the present invention advantageously provides a way to flexibly configure decentralized control on-the-fly over an existing Building Energy Management Systems (BEMS) or as a standalone system to control the air-conditioning system for optimizing a plurality of building performance parameters in providing an environment (e.g., desired indoor environment) with respect to the zone of the building with significant energy saving. Further, the method of controlling an air-conditioning system associated with a building according to various embodiments may provide a scalable and adaptive implementation architecture that supports distributed optimal control for multi-zone commercial Heating, Ventilation and Air Conditioning (HVAC) systems.

depicts a schematic block diagram of a control systemfor controlling an air-conditioning system associated with a building for optimizing a plurality of building performance parameters in providing an environment with respect to a zone of the building, according to various embodiments of the present invention, such as corresponding to the methodof controlling an air-conditioning system as described hereinbefore according to various embodiments of the present invention. The control systemcomprises a memory, and at least one processorcommunicatively coupled to the memoryand configured to: obtain zone environmental condition information including zone temperature data associated to the zone, and cooling air temperature data associated to an air handling unit associated to the zone; obtain, from a zone model generator, zone cooling load parameters associated to the zone with respect to a plurality of time periods and a zone thermal dynamic model; obtain, from a scheduler, a sequence of optimal cool air supply rates with respect to a plurality of subsequent time periods with respect to the zone determined based on a multi-component cost function including a plurality of components relating to the plurality of building performance parameters; determine, based on the zone thermal dynamic model, a sequence of zone controller set-points corresponding to sequence of optimal cool air supply rates with respect to the zone using the zone cooling load parameters, the sequence of optimal cool air supply rates, the zone temperature data and the cooling air temperature data associated to the air handling unit; and send the sequence of zone controller set-points to a zone controller for controlling a temperature of the zone.

It will be appreciated by a person skilled in the art that the at least one processormay be configured to perform the required functions or operations through set(s) of instructions (e.g., software modules) executable by the at least one processorto perform the required functions or operations. Accordingly, as shown in, the systemmay comprise a data obtaining module (or a data obtaining circuit)configured to obtain zone environmental condition information including zone temperature data associated to the zone, and cooling air temperature data associated to an air handling unit associated to the zone, obtain, from a zone model generator, zone cooling load parameters associated to the zone with respect to a plurality of time periods and a zone thermal dynamic model, and obtain, from a scheduler, a sequence of optimal cool air supply rates with respect to a plurality of subsequent time periods with respect to the zone determined based on a multi-component cost function including a plurality of components relating to the plurality of building performance parameters; a determination module (or a determination circuit)configured to determine, based on the zone thermal dynamic model, a sequence of zone controller set-points corresponding to the sequence of optimal cool air supply rates with respect to the zone using the zone cooling load parameters, the sequence of optimal cool air supply rates, the zone temperature data and the cooling air temperature data associated to the air handling unit; and a control action module (or a control action circuit)configured to send the sequence of zone controller set-points to a zone controller for controlling a temperature of the zone.

It will be appreciated by a person skilled in the art that the above-mentioned modules are not necessarily separate modules, and one or more modules may be realized by or implemented as one functional module (e.g., a circuit or a software program) as desired or as appropriate without deviating from the scope of the present invention. For example, two or more of the data obtaining module, the determination module, and the control action modulemay be realized (e.g., compiled together) as one executable software program (e.g., software application or simply referred to as an “app”), which for example may be stored in the memoryand executable by the at least one processorto perform the functions/operations as described herein according to various embodiments.

In various embodiments, the systemcorresponds to the methodas described hereinbefore with reference to, therefore, various functions or operations configured to be performed by the least one processormay correspond to various steps of the methoddescribed hereinbefore according to various embodiments, and thus need not be repeated with respect to the systemfor clarity and conciseness. In other words, various embodiments described herein in context of the methods are analogously valid for the respective systems, and vice versa.

For example, in various embodiments, the memorymay have stored therein the data obtaining module, the determination module, and/or the control action module, which respectively correspond to various steps of the methodas described hereinbefore according to various embodiments, which are executable by the at least one processorto perform the corresponding functions/operations as described herein.

A computing system, a controller, a microcontroller or any other system providing a processing capability may be provided according to various embodiments in the present disclosure. Such a system may be taken to include one or more processors and one or more computer-readable storage mediums. For example, the systemdescribed hereinbefore may include a processor (or controller)and a computer-readable storage medium (or memory)which are for example used in various processing carried out therein as described herein. A memory or computer-readable storage medium used in various embodiments may be a volatile memory, for example a DRAM (Dynamic Random Access Memory) or a non-volatile memory, for example a PROM (Programmable Read Only Memory), an EPROM (Erasable PROM), EEPROM (Electrically Erasable PROM), or a flash memory, e.g., a floating gate memory, a charge trapping memory, an MRAM (Magnetoresistive Random Access Memory) or a PCRAM (Phase Change Random Access Memory).

In various embodiments, a “circuit” may be understood as any kind of a logic implementing entity, which may be special purpose circuitry or a processor executing software stored in a memory, firmware, or any combination thereof. Thus, in an embodiment, a “circuit” may be a hard-wired logic circuit or a programmable logic circuit such as a programmable processor, e.g., a microprocessor (e.g., a Complex Instruction Set Computer (CISC) processor or a Reduced Instruction Set Computer (RISC) processor). A “circuit” may also be a processor executing software, e.g., any kind of computer program, e.g., a computer program using a virtual machine code, e.g., Java. Any other kind of implementation of the respective functions which will be described in more detail below may also be understood as a “circuit” in accordance with various alternative embodiments. Similarly, a “module” may be a portion of a system according to various embodiments in the present invention and may encompass a “circuit” as above, or may be understood to be any kind of a logic-implementing entity therefrom.

Some portions of the present disclosure are explicitly or implicitly presented in terms of algorithms and functional or symbolic representations of operations on data within a computer memory. These algorithmic descriptions and functional or symbolic representations are the means used by those skilled in the data processing arts to convey most effectively the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities, such as electrical, magnetic or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated.

Unless specifically stated otherwise, and as apparent from the following, it will be appreciated that throughout the present specification, discussions utilizing terms such as “obtaining”, “determining”, “sending”, “controlling” or the like, refer to the actions and processes of a computer system, or similar electronic device, that manipulates and transforms data represented as physical quantities within the computer system into other data similarly represented as physical quantities within the computer system or other information storage, transmission or display devices.

The present specification also discloses a system (e.g., which may also be embodied as a device or an apparatus), such as the system, for performing the operations/functions of the methods described herein. Such a system may be specially constructed for the required purposes, or may comprise a general purpose computer or other device selectively activated or reconfigured by a computer program stored in the computer. The algorithms presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose machines may be used with computer programs in accordance with the teachings herein. Alternatively, the construction of more specialized apparatus to perform the required method steps may be appropriate.

In addition, the present specification also at least implicitly discloses a computer program or software/functional module, in that it would be apparent to the person skilled in the art that the individual steps of the methods described herein may be put into effect by computer code. The computer program is not intended to be limited to any particular programming language and implementation thereof. It will be appreciated that a variety of programming languages and coding thereof may be used to implement the teachings of the disclosure contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. There are many other variants of the computer program, which can use different control flows without departing from the spirit or scope of the invention. It will be appreciated by a person skilled in the art that various modules described herein (e.g., the data obtaining module, the determination module, and/or the control action module) may be software module(s) realized by computer program(s) or set(s) of instructions executable by a computer processor to perform the required functions, or may be hardware module(s) being functional hardware unit(s) designed to perform the required functions. It will also be appreciated that a combination of hardware and software modules may be implemented.

Furthermore, one or more of the steps of a computer program/module or method described herein may be performed in parallel rather than sequentially. Such a computer program may be stored on any computer readable medium. The computer readable medium may include storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a general purpose computer. The computer program when loaded and executed on such a general-purpose computer effectively results in an apparatus that implements the steps of the methods described herein.

In various embodiments, there is provided a computer program product, embodied in one or more computer-readable storage mediums (non-transitory computer-readable storage medium), comprising instructions (e.g., the data obtaining module, the determination module, and/or the control action module) executable by one or more computer processors to perform a methodof controlling an air-conditioning system as described hereinbefore with reference to. Accordingly, various computer programs or modules described herein may be stored in a computer program product receivable by a system therein, such as the systemas shown in, for execution by at least one processorof the systemto perform the required or desired functions.

The software or functional modules described herein may also be implemented as hardware modules. More particularly, in the hardware sense, a module is a functional hardware unit designed for use with other components or modules. For example, a module may be implemented using discrete electronic components, or it can form a portion of an entire electronic circuit such as an Application Specific Integrated Circuit (ASIC). Numerous other possibilities exist. Those skilled in the art will appreciate that the software or functional module(s) described herein can also be implemented as a combination of hardware and software modules.